# Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference

arXiv: https://arxiv.org/abs/1904.09745 · HTML: https://mlsanity.com/paper/1904.09745/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 17 of 17)

- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)
- [Syntactic Control of Language Models by Posterior Inference](https://mlsanity.com/paper/2506.07154/) (2025)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Cross-Lingual Constituency Parsing for Middle High German: A Delexicalized Approach](https://mlsanity.com/paper/2308.04645/) (2023)
- [Assessment of Pre-Trained Models Across Languages and Grammars](https://mlsanity.com/paper/2309.11165/) (2023)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Neural Combinatory Constituency Parsing](https://mlsanity.com/paper/2106.06689/) (2021)
- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)
- [An Exploration of Left-Corner Transformations](https://mlsanity.com/paper/2311.16258/) (2023)
- [From Partial to Strictly Incremental Constituent Parsing](https://mlsanity.com/paper/2402.02782/) (2024)
- [Bringing Emerging Architectures to Sequence Labeling in NLP](https://mlsanity.com/paper/2509.25918/) (2025)
- [Order-sensitive Neural Constituency Parsing](https://mlsanity.com/paper/2211.00421/) (2022)
- [On Parsing as Tagging](https://mlsanity.com/paper/2211.07344/) (2022)
- [Nested Named Entity Recognition as Single-Pass Sequence Labeling](https://mlsanity.com/paper/2505.16855/) (2025)
- [Hierarchical Bracketing Encodings Work for Dependency Graphs](https://mlsanity.com/paper/2509.09388/) (2025)

## Abstract (arXiv)

We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word's tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.